Overview

Dataset statistics

Number of variables18
Number of observations3630
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory510.6 KiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical8

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Admission grade is highly overall correlated with Previous qualification (grade)High correlation
Curricular units 1st sem (approved) is highly overall correlated with Curricular units 1st sem (grade) and 3 other fieldsHigh correlation
Curricular units 1st sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 3 other fieldsHigh correlation
Curricular units 2nd sem (approved) is highly overall correlated with Curricular units 1st sem (approved) and 3 other fieldsHigh correlation
Curricular units 2nd sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 3 other fieldsHigh correlation
Previous qualification (grade) is highly overall correlated with Admission gradeHigh correlation
Target is highly overall correlated with Curricular units 1st sem (approved) and 3 other fieldsHigh correlation
Educational special needs is highly imbalanced (91.3%)Imbalance
Curricular units 1st sem (approved) has 647 (17.8%) zerosZeros
Curricular units 1st sem (grade) has 647 (17.8%) zerosZeros
Curricular units 2nd sem (approved) has 802 (22.1%) zerosZeros
Curricular units 2nd sem (grade) has 802 (22.1%) zerosZeros
Curricular units 2nd sem (without evaluations) has 3416 (94.1%) zerosZeros

Reproduction

Analysis started2026-01-12 15:20:23.301836
Analysis finished2026-01-12 15:20:56.637589
Duration33.34 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Marital status
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1842975
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:20:56.792464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61300882
Coefficient of variation (CV)0.51761387
Kurtosis20.916507
Mean1.1842975
Median Absolute Deviation (MAD)0
Skewness4.3378617
Sum4299
Variance0.37577982
MonotonicityNot monotonic
2026-01-12T20:20:56.979320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13199
88.1%
2327
 
9.0%
475
 
2.1%
522
 
0.6%
65
 
0.1%
32
 
0.1%
ValueCountFrequency (%)
13199
88.1%
2327
 
9.0%
32
 
0.1%
475
 
2.1%
522
 
0.6%
65
 
0.1%
ValueCountFrequency (%)
65
 
0.1%
522
 
0.6%
475
 
2.1%
32
 
0.1%
2327
 
9.0%
13199
88.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
3222 
0
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Length

2026-01-12T20:20:57.219132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:20:57.395020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring characters

ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13222
88.8%
0408
 
11.2%

Previous qualification
Real number (ℝ)

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5322314
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:20:57.549873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile39
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.024134
Coefficient of variation (CV)2.2117436
Kurtosis7.0651029
Mean4.5322314
Median Absolute Deviation (MAD)0
Skewness2.9042661
Sum16452
Variance100.48325
MonotonicityNot monotonic
2026-01-12T20:20:57.775716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13019
83.2%
39164
 
4.5%
19149
 
4.1%
3122
 
3.4%
1239
 
1.1%
4034
 
0.9%
4228
 
0.8%
222
 
0.6%
615
 
0.4%
911
 
0.3%
Other values (7)27
 
0.7%
ValueCountFrequency (%)
13019
83.2%
222
 
0.6%
3122
 
3.4%
47
 
0.2%
51
 
< 0.1%
615
 
0.4%
911
 
0.3%
104
 
0.1%
1239
 
1.1%
141
 
< 0.1%
ValueCountFrequency (%)
436
 
0.2%
4228
 
0.8%
4034
 
0.9%
39164
4.5%
386
 
0.2%
19149
4.1%
152
 
0.1%
141
 
< 0.1%
1239
 
1.1%
104
 
0.1%

Previous qualification (grade)
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.92061
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:20:58.039530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile110
Q1125
median133.1
Q3140
95-th percentile158
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.238373
Coefficient of variation (CV)0.09959609
Kurtosis0.89457229
Mean132.92061
Median Absolute Deviation (MAD)7.1
Skewness0.28762278
Sum482501.8
Variance175.25451
MonotonicityNot monotonic
2026-01-12T20:20:58.336250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.1426
 
11.7%
130302
 
8.3%
140271
 
7.5%
120225
 
6.2%
150137
 
3.8%
12592
 
2.5%
11088
 
2.4%
13585
 
2.3%
16079
 
2.2%
13178
 
2.1%
Other values (91)1847
50.9%
ValueCountFrequency (%)
951
 
< 0.1%
961
 
< 0.1%
971
 
< 0.1%
991
 
< 0.1%
10062
1.7%
1013
 
0.1%
1025
 
0.1%
1032
 
0.1%
1053
 
0.1%
1066
 
0.2%
ValueCountFrequency (%)
1901
 
< 0.1%
1881
 
< 0.1%
184.41
 
< 0.1%
1821
 
< 0.1%
1807
0.2%
1782
 
0.1%
1772
 
0.1%
1761
 
< 0.1%
1751
 
< 0.1%
1741
 
< 0.1%

Admission grade
Real number (ℝ)

High correlation 

Distinct602
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.29394
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:20:58.624048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.5
Q1118
median126.5
Q3135.1
95-th percentile154.21
Maximum190
Range95
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation14.611295
Coefficient of variation (CV)0.1147839
Kurtosis0.56734312
Mean127.29394
Median Absolute Deviation (MAD)8.5
Skewness0.50766763
Sum462077
Variance213.48995
MonotonicityNot monotonic
2026-01-12T20:20:59.066675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130125
 
3.4%
120121
 
3.3%
140117
 
3.2%
10093
 
2.6%
15067
 
1.8%
11065
 
1.8%
16038
 
1.0%
128.235
 
1.0%
12825
 
0.7%
12723
 
0.6%
Other values (592)2921
80.5%
ValueCountFrequency (%)
9510
0.3%
95.11
 
< 0.1%
95.52
 
0.1%
95.81
 
< 0.1%
965
0.1%
96.11
 
< 0.1%
96.71
 
< 0.1%
975
0.1%
97.21
 
< 0.1%
97.41
 
< 0.1%
ValueCountFrequency (%)
1902
0.1%
184.41
< 0.1%
1841
< 0.1%
183.51
< 0.1%
180.41
< 0.1%
1802
0.1%
179.61
< 0.1%
178.31
< 0.1%
1781
< 0.1%
176.71
< 0.1%

Displaced
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
1993 
0
1637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Length

2026-01-12T20:20:59.414403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:20:59.576277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring characters

ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11993
54.9%
01637
45.1%

Educational special needs
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
3590 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Length

2026-01-12T20:20:59.786108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:20:59.935989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring characters

ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03590
98.9%
140
 
1.1%

Debtor
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
3217 
1
413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Length

2026-01-12T20:21:00.129862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:21:00.280764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring characters

ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03217
88.6%
1413
 
11.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
3144 
0
486 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Length

2026-01-12T20:21:00.472567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:21:00.623450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring characters

ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13144
86.6%
0486
 
13.4%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
2381 
1
1249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Length

2026-01-12T20:21:00.820339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:21:00.971178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring characters

ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02381
65.6%
11249
34.4%

Scholarship holder
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
0
2661 
1
969 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Length

2026-01-12T20:21:01.468785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:21:01.614672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring characters

ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02661
73.3%
1969
 
26.7%

Age at enrollment
Real number (ℝ)

Distinct46
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.461157
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:01.822504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.8279942
Coefficient of variation (CV)0.33365764
Kurtosis3.8030507
Mean23.461157
Median Absolute Deviation (MAD)2
Skewness1.9907248
Sum85164
Variance61.277493
MonotonicityNot monotonic
2026-01-12T20:21:02.105283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18864
23.8%
19754
20.8%
20459
12.6%
21252
 
6.9%
22137
 
3.8%
24101
 
2.8%
2384
 
2.3%
2779
 
2.2%
2675
 
2.1%
2572
 
2.0%
Other values (36)753
20.7%
ValueCountFrequency (%)
173
 
0.1%
18864
23.8%
19754
20.8%
20459
12.6%
21252
 
6.9%
22137
 
3.8%
2384
 
2.3%
24101
 
2.8%
2572
 
2.0%
2675
 
2.1%
ValueCountFrequency (%)
701
 
< 0.1%
621
 
< 0.1%
611
 
< 0.1%
602
 
0.1%
593
0.1%
583
0.1%
572
 
0.1%
555
0.1%
546
0.2%
536
0.2%

Curricular units 1st sem (approved)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7914601
Minimum0
Maximum26
Zeros647
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:02.353086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2378452
Coefficient of variation (CV)0.67575335
Kurtosis2.8666924
Mean4.7914601
Median Absolute Deviation (MAD)1
Skewness0.75417833
Sum17393
Variance10.483642
MonotonicityNot monotonic
2026-01-12T20:21:02.574937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61033
28.5%
0647
17.8%
5530
14.6%
7429
11.8%
4288
 
7.9%
3176
 
4.8%
2118
 
3.3%
194
 
2.6%
894
 
2.6%
1142
 
1.2%
Other values (13)179
 
4.9%
ValueCountFrequency (%)
0647
17.8%
194
 
2.6%
2118
 
3.3%
3176
 
4.8%
4288
 
7.9%
5530
14.6%
61033
28.5%
7429
11.8%
894
 
2.6%
935
 
1.0%
ValueCountFrequency (%)
261
 
< 0.1%
214
 
0.1%
203
 
0.1%
192
 
0.1%
1815
0.4%
1710
0.3%
165
 
0.1%
156
 
0.2%
1414
0.4%
1323
0.6%

Curricular units 1st sem (grade)
Real number (ℝ)

High correlation  Zeros 

Distinct752
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.53486
Minimum0
Maximum18.875
Zeros647
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:02.831708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.341429
Q313.5
95-th percentile15
Maximum18.875
Range18.875
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.057694
Coefficient of variation (CV)0.48009125
Kurtosis0.48687085
Mean10.53486
Median Absolute Deviation (MAD)1.1985714
Skewness-1.4518527
Sum38241.54
Variance25.580268
MonotonicityNot monotonic
2026-01-12T20:21:03.141465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0647
 
17.8%
12153
 
4.2%
13123
 
3.4%
1195
 
2.6%
1470
 
1.9%
12.3333333368
 
1.9%
12.6666666767
 
1.8%
12.566
 
1.8%
11.564
 
1.8%
1060
 
1.7%
Other values (742)2217
61.1%
ValueCountFrequency (%)
0647
17.8%
9.81
 
< 0.1%
1060
 
1.7%
10.166666671
 
< 0.1%
10.23
 
0.1%
10.214285711
 
< 0.1%
10.257
 
0.2%
10.285714291
 
< 0.1%
10.333333337
 
0.2%
10.368421051
 
< 0.1%
ValueCountFrequency (%)
18.8751
 
< 0.1%
182
0.1%
17.333333332
0.1%
17.1251
 
< 0.1%
17.111111111
 
< 0.1%
17.005555561
 
< 0.1%
173
0.1%
16.91
 
< 0.1%
16.885714291
 
< 0.1%
16.857142861
 
< 0.1%

Curricular units 2nd sem (approved)
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5184573
Minimum0
Maximum20
Zeros802
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:03.394294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1623763
Coefficient of variation (CV)0.69987964
Kurtosis0.66566854
Mean4.5184573
Median Absolute Deviation (MAD)2
Skewness0.26819875
Sum16402
Variance10.000624
MonotonicityNot monotonic
2026-01-12T20:21:03.624130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6867
23.9%
0802
22.1%
5569
15.7%
8306
 
8.4%
7287
 
7.9%
4254
 
7.0%
3161
 
4.4%
2121
 
3.3%
186
 
2.4%
1144
 
1.2%
Other values (10)133
 
3.7%
ValueCountFrequency (%)
0802
22.1%
186
 
2.4%
2121
 
3.3%
3161
 
4.4%
4254
 
7.0%
5569
15.7%
6867
23.9%
7287
 
7.9%
8306
 
8.4%
925
 
0.7%
ValueCountFrequency (%)
202
 
0.1%
193
 
0.1%
182
 
0.1%
178
 
0.2%
162
 
0.1%
146
 
0.2%
1321
0.6%
1232
0.9%
1144
1.2%
1032
0.9%

Curricular units 2nd sem (grade)
Real number (ℝ)

High correlation  Zeros 

Distinct724
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.036155
Minimum0
Maximum18.571429
Zeros802
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:03.896918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.517857
median12.333333
Q313.5
95-th percentile15.006111
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.9821429

Descriptive statistics

Standard deviation5.4817421
Coefficient of variation (CV)0.54619942
Kurtosis-0.36292579
Mean10.036155
Median Absolute Deviation (MAD)1.3333333
Skewness-1.167812
Sum36431.243
Variance30.049496
MonotonicityNot monotonic
2026-01-12T20:21:04.191683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0802
 
22.1%
12122
 
3.4%
13117
 
3.2%
11111
 
3.1%
1463
 
1.7%
11.562
 
1.7%
12.555
 
1.5%
1051
 
1.4%
13.550
 
1.4%
12.6666666749
 
1.3%
Other values (714)2148
59.2%
ValueCountFrequency (%)
0802
22.1%
1051
 
1.4%
10.166666673
 
0.1%
10.22
 
0.1%
10.255
 
0.1%
10.3333333310
 
0.3%
10.44
 
0.1%
10.428571431
 
< 0.1%
10.444444441
 
< 0.1%
10.529
 
0.8%
ValueCountFrequency (%)
18.571428571
< 0.1%
17.714285711
< 0.1%
17.692307691
< 0.1%
17.61
< 0.1%
17.58751
< 0.1%
17.428571431
< 0.1%
17.166666671
< 0.1%
171
< 0.1%
16.909090911
< 0.1%
16.82
0.1%
Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14214876
Minimum0
Maximum12
Zeros3416
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2026-01-12T20:21:04.420462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.74767044
Coefficient of variation (CV)5.2597746
Kurtosis73.264171
Mean0.14214876
Median Absolute Deviation (MAD)0
Skewness7.6154483
Sum516
Variance0.55901109
MonotonicityNot monotonic
2026-01-12T20:21:04.630295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
03416
94.1%
1107
 
2.9%
235
 
1.0%
323
 
0.6%
516
 
0.4%
416
 
0.4%
67
 
0.2%
74
 
0.1%
84
 
0.1%
122
 
0.1%
ValueCountFrequency (%)
03416
94.1%
1107
 
2.9%
235
 
1.0%
323
 
0.6%
416
 
0.4%
516
 
0.4%
67
 
0.2%
74
 
0.1%
84
 
0.1%
122
 
0.1%
ValueCountFrequency (%)
122
 
0.1%
84
 
0.1%
74
 
0.1%
67
 
0.2%
516
 
0.4%
416
 
0.4%
323
 
0.6%
235
 
1.0%
1107
 
2.9%
03416
94.1%

Target
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size205.7 KiB
1
2209 
0
1421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Length

2026-01-12T20:21:04.896086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T20:21:05.046964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring characters

ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12209
60.9%
01421
39.1%

Interactions

2026-01-12T20:20:52.871600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:25.315252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:28.034111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:30.221437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:32.604511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:34.818767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:37.155956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:40.748097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:44.091469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:48.606914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:53.126352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:25.683962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:28.261930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:30.505165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:32.811351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:35.031600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:37.649539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:41.023883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:44.418210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:49.449249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:53.363166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:25.890799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:28.458778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:30.721025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:33.003198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:35.239437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:37.851379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:41.306660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:44.716975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:50.168681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:53.602023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:26.103657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:28.673655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:30.928878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:33.223027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:35.449302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:38.080200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:41.573450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:45.247556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:50.653301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:53.824847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:26.297527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:28.880474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:31.163651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:33.413878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:35.652115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:38.283086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:41.827253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:45.527334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:50.989039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:54.067611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:26.512310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:29.098273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:31.419445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:33.667677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:35.939888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:38.506868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:42.246919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:45.845084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:51.257830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:54.326409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:26.724191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:29.314106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:31.709242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:33.902538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:36.221664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:39.295244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:42.659593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:46.140879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:51.505628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:54.782049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:26.952962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:29.545924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:31.937037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:34.128317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:36.448488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:39.842810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:42.934377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:46.443614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:51.784437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:55.089806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:27.418597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:29.779736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:32.170854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:34.367127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:36.693296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:40.129589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:43.458966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:46.755372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:52.084172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:55.362589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:27.711365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:29.993567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:32.392724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:34.591948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:36.926156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:40.446340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:43.801699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:47.349903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T20:20:52.324009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-12T20:21:05.334739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Admission gradeAge at enrollmentCurricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Daytime/evening attendanceDebtorDisplacedEducational special needsGenderMarital statusPrevious qualificationPrevious qualification (grade)Scholarship holderTargetTuition fees up to date
Admission grade1.000-0.1120.1120.2290.1110.209-0.0260.1120.0680.1390.0000.043-0.0020.1160.5820.0950.1570.088
Age at enrollment-0.1121.000-0.204-0.243-0.222-0.2470.1160.4810.1400.4020.0000.1950.4880.408-0.1700.2400.3330.232
Curricular units 1st sem (approved)0.112-0.2041.0000.6510.9090.682-0.0590.1850.1670.1530.0000.264-0.074-0.0590.0990.2900.6490.327
Curricular units 1st sem (grade)0.229-0.2430.6511.0000.6360.790-0.0450.1390.1200.0910.0000.210-0.099-0.0620.2020.2090.5420.277
Curricular units 2nd sem (approved)0.111-0.2220.9090.6361.0000.711-0.0580.1080.2020.1290.0000.275-0.081-0.0670.0890.2890.7360.364
Curricular units 2nd sem (grade)0.209-0.2470.6820.7900.7111.000-0.0590.0940.1600.0830.0000.222-0.097-0.0630.1720.2270.6400.327
Curricular units 2nd sem (without evaluations)-0.0260.116-0.059-0.045-0.058-0.0591.0000.0000.0870.0280.0000.0430.0590.061-0.0240.0320.1010.090
Daytime/evening attendance\t0.1120.4810.1850.1390.1080.0940.0001.0000.0000.2420.0190.0240.3560.1600.1220.1080.0820.048
Debtor0.0680.1400.1670.1200.2020.1600.0870.0001.0000.0910.0000.0490.0320.1490.0720.0620.2660.433
Displaced0.1390.4020.1530.0910.1290.0830.0280.2420.0911.0000.0000.1260.2790.1840.1040.0840.1240.103
Educational special needs0.0000.0000.0000.0000.0000.0000.0000.0190.0000.0001.0000.0000.0000.0000.0000.0230.0000.000
Gender0.0430.1950.2640.2100.2750.2220.0430.0240.0490.1260.0001.0000.0560.1270.0420.1870.2510.120
Marital status-0.0020.488-0.074-0.099-0.081-0.0970.0590.3560.0320.2790.0000.0561.0000.198-0.0400.1170.1150.104
Previous qualification0.1160.408-0.059-0.062-0.067-0.0630.0610.1600.1490.1840.0000.1270.1981.0000.0440.0850.1520.124
Previous qualification (grade)0.582-0.1700.0990.2020.0890.172-0.0240.1220.0720.1040.0000.042-0.0400.0441.0000.0660.1340.105
Scholarship holder0.0950.2400.2900.2090.2890.2270.0320.1080.0620.0840.0230.1870.1170.0850.0661.0000.3120.168
Target0.1570.3330.6490.5420.7360.6400.1010.0820.2660.1240.0000.2510.1150.1520.1340.3121.0000.441
Tuition fees up to date0.0880.2320.3270.2770.3640.3270.0900.0480.4330.1030.0000.1200.1040.1240.1050.1680.4411.000

Missing values

2026-01-12T20:20:55.789282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-12T20:20:56.298856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital statusDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Admission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentCurricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Target
0111122.0127.31001102000.00000000.00000000
1111160.0142.510001019614.000000613.66666701
2111122.0124.81000101900.00000000.00000000
3111122.0119.610010020613.428571512.40000001
4201100.0141.500010045512.333333613.00000001
52019133.1114.800111050511.857143511.50000051
6111142.0128.410010118713.300000814.34500001
7111119.0113.11000102200.00000000.00000000
8111137.0129.300010121613.875000614.14285701
9111138.0123.010100018511.400000213.50000000
Marital statusDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Admission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentCurricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Target
3620111137.0129.310010018511.800000511.60000001
36214119133.1117.800100046312.333333311.08333300
3622111136.0131.3000100231212.6250001212.62500011
3623111132.0133.810010120613.833333613.50000001
36241139120.0120.000011020612.500000713.14285711
3625111125.0122.200011019513.600000512.66666701
3626111120.0119.010100018612.000000211.00000000
3627111154.0149.510010130714.912500113.50000000
3628111180.0153.810010120513.800000512.00000001
3629111152.0152.010010022611.666667613.00000001

Duplicate rows

Most frequently occurring

Marital statusDaytime/evening attendancePrevious qualificationPrevious qualification (grade)Admission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentCurricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Target# duplicates
0111133.195.00001102000.000.0002